Development of the Inversion Method for Transient Electromagnetic Data

Xue, Gang; Hai, Li; He, Yiming; Xue, Junjie; Wu, Xiaofeng

doi:10.1109/access.2020.3013626

Cited by 35 publications

(12 citation statements)

References 67 publications

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“…The helicopter transient electromagnetic (HTEM) system is widely used in mineral exploration, groundwater surveys, polar exploration, urban construction, tunnel prediction, substation grounding grid diagnosis, and other fields because its detection process is not affected by the geological environment. Generally, the system consists of a bipolar current pulse transmitter, a multi-channel receiver, a transmitting coil, and a low-noise sensing coil [ 1 , 2 , 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Nine-Level Inverter with Adjustable Turn-Off Time for Helicopter Transient Electromagnetic Detection

Peng

Guo

Chang

et al. 2023

Sensors

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The current inverter is the core component of the helicopter transient electromagnetic (HTEM) detection system. It should meet the concerns of low loss, high power, and fast turn-OFF time. This article proposes a new circuit topology based on nine-level inverter technology to overcome the drawbacks of typical PWM (pulse width modulation) inverters, such as switching losses and harmonics. This circuit topology overcomes the shortcomings of the traditional single constant voltage clamp circuit in which the turn-OFF time is not adjustable. Using an inverter with the proposed topology is able to avoid the complex PWM control method and switching loss. In this way, the current rising edge and falling edge of this inverter are also improved effectively. The proposed inverter has adjustable turn-ON-time and turn-OFF time, which is significantly different from the conventional single-clamp inverter. Through subsequent experiments, the inverter proved to have the capability of generating trapezoidal current waveforms. Moreover, by modifying the FPGA (Field Programmable Gate Array) control program, three different turn-OFF times are achieved. The nine-level inverter has a peak current of 1.5 A with an adjustable turn-OFF time from 129 μs to 162 μs. Moreover, the switching frequency of the inverter is reduced from 10 kHz to below 100 Hz. The experimental results further demonstrate that it achieves lower switching losses and more flexible transmission. Our work in this article provides an efficient way to improve the performance of HTEM detection systems.

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Section: Introductionmentioning

confidence: 99%

A Nine-Level Inverter with Adjustable Turn-Off Time for Helicopter Transient Electromagnetic Detection

Peng

Guo

Chang

et al. 2023

Sensors

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“…Therefore, a DL method could be beneficial in surrogating well-known EM processes, e.g. forward modelling where the propagation of the EM fields is simulated, resulting in the forward responses (Xue et al, 2020), and inverse modelling (inversion) where the electrical resistivity properties of the subsurface are deduced from observed EM data (Zhdanov, 2015). DL methods can also assist with manual tasks, which may require considerable time when performed manually, such as anomaly detection in EM data.…”

Section: Introductionmentioning

confidence: 99%

DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications

Asif

Foged

Bording

et al. 2023

Earth Syst. Sci. Data

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Abstract. Deep learning (DL) algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale datasets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database (RMD) with a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. Potentially, the presented database can be used to build surrogate models of well-known processes and to aid in labour-intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, incorporate consistency and credibility into deep learning models. We show the effectiveness of the presented database by surrogating the forward-modelling process, and we urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database. The dataset is publicly available at https://doi.org/10.5281/zenodo.7260886 (Asif et al., 2022a).

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“…The principle of GPR is to transmit electromagnetic wave signals through a transmitting antenna and then identify structures such as karst and water-rich areas by analyzing the waveform of electromagnetic wave propagation [16]. TEM is widely used to predict water enrichment because it can obtain the apparent resistivity distribution in front of the tunnel face according to the principle of electromagnetic induction [17]. Different geophysical methods have various advantages, disadvantages, prediction ranges, and accuracies.…”

Section: Introductionmentioning

confidence: 99%

Improved Dempster–Shafer Evidence Theory for Tunnel Water Inrush Risk Analysis Based on Fuzzy Identification Factors of Multi-Source Geophysical Data

Ding

Yang

et al. 2022

Remote Sensing

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Water inrush is one of the most important risk factors in tunnel construction because of its abruptness and timeliness. Various geophysical data used in actual construction contain useful information related to groundwater development. However, the existing approaches with such data from multiple sources and sensors are generally independent and cannot integrate this information, leading to inaccurate projections. In addition, existing tunnel advanced geological forecast reports for risk projections interpreted by human operators generally contain no quantitative observations or measurements, but only consist of ambiguous and uncertain qualitative descriptions. To surmount the problems above, this paper proposes a tunnel water inrush risk analysis method by fusing multi-source geophysical observations with fuzzy identification factors. Specifically, the membership function of the fuzzy set is used to solve the difficulty in determining the basic probability assignment function in the improved Dempster–Shafer evidence theory. The prediction model of effluent conditions fuses seismic wave reflection data, ground penetrating radar data, and transient electromagnetic data. Therefore, quantitative evaluations of the effluent conditions are achieved, including the strand water, linear water, seepage and dripping water, and anhydrous. Experimental evaluations with a typical tunnel section were conducted, in which the state of the groundwater from a series of geological sketch reports in this sectionpaper were used as ground truth for verification. The experimental results revealed that the proposed method not only has high accuracy and robustness but also aligns well with different evidence effectively that generally contradicts manual interpretation reports. The results from 12 randomly selected tunnel sections also demonstrate the generalization abilities of the proposed method.

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Development of the Inversion Method for Transient Electromagnetic Data

Cited by 35 publications

References 67 publications

A Nine-Level Inverter with Adjustable Turn-Off Time for Helicopter Transient Electromagnetic Detection

A Nine-Level Inverter with Adjustable Turn-Off Time for Helicopter Transient Electromagnetic Detection

DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications

Improved Dempster–Shafer Evidence Theory for Tunnel Water Inrush Risk Analysis Based on Fuzzy Identification Factors of Multi-Source Geophysical Data

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